Quality control methods

Quality control turns ChatGPT from a risky tool into a reliable assistant. Start by establishing verification layers for different content types. Product specs need technical review, user stories require edge case analysis, and market insights demand source validation. Each output type gets its own quality checklist.

Create systematic review processes that catch AI blind spots. Technical reviewers check feasibility and constraints. UX designers validate user flows and accessibility. Legal examines compliance implications. This multi-perspective approach ensures comprehensive coverage.

Implement feedback loops that improve future prompts. Document which ChatGPT outputs required heavy revision and why. Track patterns in AI mistakes. Does it consistently miss security requirements or oversimplify integrations? Use these insights to refine prompts and prevent common issues.